Digital transformation can propel operational performance to new levels, but to truly unlock its full potential and get tangible results, a strong foundation of operational data is required. Unfortunately, many companies are constrained by a lack of data standardization and coverage when it comes to their data and operational technology investments. This stems from operational data being scattered across multiple systems and difficult to access; not all critical data being logged into databases; and a general disconnect between the areas of operational technology (OT) and information technology (IT). In this session, you’ll learn how you can leverage your existing infrastructure to enable effective data management so that you can implement and scale digital solutions that deliver real performance gains. Discover how to establish and maintain a data foundation that supports your digital transformation initiatives by:
- Improving Data Quality
- Centralizing Data
- Contextualizing Data
- Establishing Data Policies.
Running a highly successful company is no longer about the systems and procedures of the past. Peak performance comes from aggregating, organizing, clarifying, and comprehending data to squeeze every bit of performance potential out of the hardware and software solutions that plants rely on for visibility of operations.
Dapo opened highlighting each of these four strategies. On data quality, it’s important to ensure the data is available in a timely fashion for use with analytics applications. This data needs to be surfaced for analytics at the highest possible precision.
Data centralization is needed for easy accessibility that makes finding, analyzing and visualizing information across sources much easier. It’s important to be a single source of truth for all operational data used by business stakeholders and consumers. From the operational data lakes that combine real-time and derived data, this data is contextualized into an OT data stream and send to the enterprise data lake that combines it with IT data sources.
Stephen noted that contextualization provides asset-centric data sets to analytics and visualization tiers and applies standardizations to enable apples-to-apples comparisons across sites and facilities.
Data governance ensures the data standards, policies, guidelines and procedures developed to achieve a high data quality are maintained. This governance helps to improve innovation through cross-functional discussions among business units within the organization.
Visit the Data Management section on Emerson.com for ways to put these recommendations into practice to build a stronger data foundation and to help drive better decisions through more actionable information.